New PDF release: Quantitative Trait Loci Analysis in Animals, 2nd Edition

By Joel Ira Weller

ISBN-10: 1845934679

ISBN-13: 9781845934675

Quantitative Trait Loci (QTL) is a subject of significant agricultural value for effective farm animals construction. This advanced-level textbook covers the entire statistical equipment which were used or proposed for detection and research of QTL and marker- and gene- assisted choice in animal genetics and breeding, in addition to new advances that experience revolutionized the sphere because the first version.

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Additional resources for Quantitative Trait Loci Analysis in Animals, 2nd Edition (Modular Texts)

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None of these methods guarantee that, even if a maximum is found, it will be the global maximum. In the final sections we considered Bayesian estimation and the method of minimal distance. Solutions by these methods, like ML, can only be computed iteratively. Application of these methods to estimation of QTL parameters will be considered in detail in Chapters 5–7. 1 Random and Fixed Effects, the Mixed Model Introduction So far we have assumed that all effects included in the model, except for the residual, are fixed.

They called this model the ‘reduced animal model’ (RAM). If the number of individuals without progeny included in the analysis is relatively large, compared to the number of individuals with progeny, then there can be a substantial reduction in the total number of equations in the model. For individuals without progeny this can also be considered a ‘gametic’ model, because 32 Chapter 3 the effects included in this model are the contributions of the paternal and maternal gametes to each progeny.

It is then necessary to solve a system of equations equal to the number of parameters being estimated. In general, the likelihood function for estimation of m parameters (θ1 , θ2 , . . , θm ), from a sample of N observations (y1 , y2 , . . , yN ) can be written as follows: L = p(y1 , y2 , . yn |θ1 , θ2 , . θm ) = p(y1 |θ1 , θ2 , . θm )p(y2 |θ1 , θ2 , . θm ). p(yn |θ1 , θ2 , . θm ) = p(yi |θ1 , θ2 , . 23) where p(yi |θ) represents the probability of obtaining yi , conditional on the vector of parameters.

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Quantitative Trait Loci Analysis in Animals, 2nd Edition (Modular Texts) by Joel Ira Weller


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